Three Ways AI is Reshaping Supply Chain and Logistics

Artificial Intelligence is picking up momentum in supply chain management and logistics. Research by Forbes Insights demonstrates that relatively 65% of high-ranking transportation-centered executives believe that transportation, logistics and supply chain processes are going through an intense transformation. The evolution of artificial intelligence (AI), machine learning(ML) and other technologies carry great potential for disruption and innovation and are driving change within the industry today.

AI computing techniques, for instance, machine learning, deep learning and natural language processing (NLP) help streamline the process of scrutinizing large volumes of data amassed from the supply chain and logistics. These techniques, when employed, deliver a sophisticated and refined analysis, initiate a function or an event centered around the results of the analysis, provide requested information, and perform several other complex functions.

Read more: Three Applications of Big Data in Logistics and Transportation

Organizations are already starting to drive revenue from AI investments.  According to  State of Artificial Intelligence for Enterprises report by Teradata, the top three areas where businesses are driving revenue include product innovation/research and development, customer service, and supply chain and operations.

Here are three ways AI can revolutionize supply chain management and logistics;

Operational cost reduction and responsiveness

AI facilitates contextual intelligence and provides insights and knowledge into determining influential factors that reduce operations and inventory costs and improve responsiveness. Organizations embracing machine learning and other AI technologies are likely to benefit more from the insights into a variety of aspects including logistics and warehouse management, collaboration, and supply chain management.

A collaborative report by DHL and IBM, Artificial Intelligence in Logistics, highlighted a number of useful AI applications in logistics including;

  • Intelligent Robotic Sorting – an effective, high-speed sorting of unstructured parcels, letters, and palletized shipments with the help of AI-driven robots that reduces human effort and error rates. Key Player: ZenRobotics
  • AI-Powered Visual Inspection – a high potential area for AI in the logistics operational environment using AI-powered cognitive visual recognition capabilities to do maintenance of physical assets. Key Player: IBM Watson

Read more: Five Major AI Trends to Watch in the Years Ahead [Infographic]

Efficient supplier relationship management

AI can improve supplier selection and increase the effectiveness of supplier relationship management. "Supplier-related risks are a major consideration for logistics professionals,” says Darrin Mackay, a logistics expert at A-Writer. “Just one mistake on the part of a supplier, and a company’s reputation can be damaged significantly.”

AI can analyze supplier-related data such as on-time in-full delivery performance, audits, evaluations, and credit scoring and provide information to use for future decisions regarding certain suppliers. As the result, a company can make better supplier decisions and improve its customer service.

Enhanced Customer Experience

AI changes relationships between logistics providers and customers by personalizing them. A great example of personalized customer experience is DHL Parcel’s cooperation with Amazon. The delivery company offered a voice-based service to track parcels and get shipment information using Amazon’s Alexa-powered Echo.

A customer can query Alexa to find out the current whereabouts of their shipment. If there was a problem with the shipment, Echo users could also ask DHL for assistance and be redirected to the customer assistance department of the company.

Read more: Smarter IoT with AI – Three Real-World Examples

The enthusiasm for AI is well-founded and the value, while lacking in some areas, is evident in other areas (like pattern recognition and machine learning). The technology already plays a significant role in some of today’s advanced supply chain and logistics solutions, increasing effectiveness, efficiency, and automating many tasks for supply chain managers and planners.

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